The present invention relates to machine learning, and more specifically, this invention relates to adaptive learning of actionable statement patterns in conversational communications.
The advent of electronic communications has been a great boon for productivity, creativity, collaboration, and a host of other well-established benefits across almost every sector of the world economy. Typical electronic communications may involve initiating and exchanging information about work activities in conversations with others over channels such as email, chat, and messaging. A major part of employees' conversations entail statements on what work (actions) gets done, and by whom.
However, the increased frequency and speed at which communications are exchanged has generally resulted in a drastic increase in the volume of communication exchanged. This, in turn, generally results in individual communicators, especially humans, receiving a volume of communications which is impossible or impractical to process effectively and efficiently within the time constraints imposed by traditional work schedules. As a result, important action items represented in the communications backlog are often missed, detrimentally impacting the parties with an interest in the action items being efficiently and effectively completed.
To address this issue, various communication tools such as email programs, electronic calendars, etc. include features which allow a user to “flag” certain communications for future follow-up action, and to indicate priority of certain communications, in order to assist the user organize the various action items represented/contained in the user's communication backlog. However, these manual organization techniques and tools still rely on the user to process the information content represented in the communications, decide the appropriate action, and take that action (e.g. set a reminder, priority level, etc.). Therefore, these manual organization techniques assist the user's organization of the voluminous communications, but detrimentally do so by adding more tasks for the user to perform to achieve the organization, and provide no assistance with respect to communications the user fails to process and manually organize.
Automating the process of parsing and organizing communications, e.g. using natural language processing (NLP) techniques that leverage machine learning principles, also prove inadequate tools to solve the problem. While the automated nature of these techniques relieves the user from having to manually process and organize the voluminous body of incoming communications, these techniques suffer from limitations inherent to machine learning. For instance, the accuracy of the automated processing algorithm is a function of the propriety of the training set used to teach the machine how to parse the communications. Where so much of the informational content and meaning of modern communications is represented in the context of the communication—both the context of the text making up the communication and the context in which the communication is exchanged—the statistical approaches employed in modern machine learning are incapable of adapting to the wide range of contexts necessary for effective and efficient automation of processing modern communications.
For instance, actionable statements are often expressed in a variety of forms (e.g. patterns) and/or language structures, and may follow different styles due to personal preference, geographic location, culture, context, and complexity of the language in which the communication is being exchanged. From a language point of view, not all communications come in complete and well-formed sentences, but sometimes include grammatical and/or syntax errors, or include incomplete sentences. In addition, sometimes actions may be implied rather than explicitly stated. Further, many scenarios may require parsing several statements or interactions to identify an actionable statement.
Moreover, finding and training, or manually encoding, comprehensive and generic patterns and rules that cover all possible actions, contexts, etc., yet precisely and specifically capture actionable statements is an exceedingly difficult, if not impossible task. In addition, these conventional approaches are not scalable as new forms are encountered in new communications, contexts (e.g. different sectors of the economy often use different jargon and attach different meanings to the same terms), new communicators become involved in the exchange of communications, etc.
As a result, communications are often improperly handled by automated techniques, resulting in sometimes humorous misunderstandings, as exemplified by personal digital assistants, predictive dictionaries, etc. However, these limitations result in real and significant economic losses as communications are improperly handled, and thus action items are missed or mishandled.
In the particular context of email, conventional techniques have focused exclusively on the content of the email message, identity and/or location (e.g. IP address) of the sender to classify emails, e.g. for spam or malware detection. However, these approaches are limited in that the context of the email cannot be fully understood from the content of the email alone. For instance, contextual information regarding the recipient, e.g. the importance of the email and/or appropriate actions to be taken cannot be readily determined from the content included by the sender or the identity/location of the sender.
Accordingly, it would be of great utility to provide systems, techniques, and computational tools capable of automatically interpreting communications using appropriate contextual knowledge regarding the actions taken in response to receiving emails, while overcoming the limitations of the conventional approaches set forth above.